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如何利用微信监管你的TF训练_[#第一枪]

发布时间:2021-06-07 12:08:24 阅读: 来源:塑料网厂家

雷锋网按:本文作者Coldwings,本文整理自作者在知乎发布的文章《利用微信监管你的TF训练》,获其授权发布。

之前回答问题【在机器学习模型的训练期间,大概几十分钟到几小时不等,大家都会在等实验的时候做什么?】的时候,说到可以用微信来管着训练,完全不用守着。没想到这么受欢迎……

原问题下的回答如下

不知道有哪些朋友是在TF/keras/chainer/mxnet等框架下用python撸的….…

这可是python啊……上itchat,弄个微信号加自己为好友(或者自己发自己),训练进展跟着一路发消息给自己就好了,做了可视化的话顺便把图也一并发过来。

然后就能安心睡觉/逛街/泡妞/写答案了。

讲道理,甚至简单的参数调整都可以照着用手机来……

大体效果如下

当然可以做得更全面一些。最可靠的办法自然是干脆地做一个http服务或者一个rpc,然而这样往往太麻烦。本着简单高效的原则,几行代码能起到效果方便自己当然是最好的,接入微信或者web真就是不错的选择了。只是查看的话,TensorBoard就很好,但是如果想加入一些自定义操作,还是自行定制的。echat.js做成web,或者itchat做个微信服务,都是挺不赖的选择。

正文如下

这里折腾一个例子。以TensorFlow的example中,利用CNN处理MNIST的程序为例,我们做一点点小小的修改。

首先这里放上写完的代码:

#!/usr/bin/env python

# coding: utf-8

'''

A Convolutional Network implementation example using TensorFlow library.

This example is using the MNIST database of handwritten digits

(http://yann.lecun.com/exdb/mnist/)

Author: Aymeric Damien

Project: https://github.com/aymericdamien/TensorFlow-Examples/

Add a itchat controller with multi thread

'''

from __future__ import print_function

import tensorflow as tf

# Import MNIST data

from tensorflow.examples.tutorials.mnist import input_data

# Import itchat & threading

import itchat

import threading

# Create a running status flag

lock = threading.Lock()

running = False

# Parameters

learning_rate = 0.001

training_iters = 200000

batch_size = 128

display_step = 10

def nn_train(wechat_name, param):

global lock, running

# Lock

with lock:

running = True

# mnist data reading

mnist = input_data.read_data_sets("data/", one_hot=True)

# Parameters

# learning_rate = 0.001

# training_iters = 200000

# batch_size = 128

# display_step = 10

learning_rate, training_iters, batch_size, display_step = param

# Network Parameters

n_input = 784 # MNIST data input (img shape: 28*28)

n_classes = 10 # MNIST total classes (0-9 digits)

dropout = 0.75 # Dropout, probability to keep units

# tf Graph input

x = tf.placeholder(tf.float32, [None, n_input])

y = tf.placeholder(tf.float32, [None, n_classes])

keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)

# Create some wrappers for simplicity

def conv2d(x, W, b, strides=1):

# Conv2D wrapper, with bias and relu activation

x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')

x = tf.nn.bias_add(x, b)

return tf.nn.relu(x)

def maxpool2d(x, k=2):

# MaxPool2D wrapper

return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],

padding='SAME')

# Create model

def conv_net(x, weights, biases, dropout):

# Reshape input picture

x = tf.reshape(x, shape=[-1, 28, 28, 1])

# Convolution Layer

conv1 = conv2d(x, weights['wc1'], biases['bc1'])

# Max Pooling (down-sampling)

conv1 = maxpool2d(conv1, k=2)

# Convolution Layer

conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])

# Max Pooling (down-sampling)

conv2 = maxpool2d(conv2, k=2)

# Fully connected layer

# Reshape conv2 output to fit fully connected layer input

fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])

fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])

fc1 = tf.nn.relu(fc1)

# Apply Dropout

fc1 = tf.nn.dropout(fc1, dropout)

# Output, class prediction

out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])

return out

# Store layers weight & bias

weights = {

# 5x5 conv, 1 input, 32 outputs

'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),

# 5x5 conv, 32 inputs, 64 outputs

'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),

# fully connected, 7*7*64 inputs, 1024 outputs

'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),

# 1024 inputs, 10 outputs (class prediction)

'out': tf.Variable(tf.random_normal([1024, n_classes]))

}

biases = {

'bc1': tf.Variable(tf.random_normal([32])),

'bc2': tf.Variable(tf.random_normal([64])),

'bd1': tf.Variable(tf.random_normal([1024])),

'out': tf.Variable(tf.random_normal([n_classes]))

}

# Construct model

pred = conv_net(x, weights, biases, keep_prob)

# Define loss and optimizer

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables

init = tf.global_variables_initializer()

# Launch the graph

with tf.Session() as sess:

sess.run(init)

step = 1

# Keep training until reach max iterations

print('Wait for lock')

with lock:

run_state = running

print('Start')

while step * batch_size < training_iters and run_state:

batch_x, batch_y = mnist.train.next_batch(batch_size)

# Run optimization op (backprop)

sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,

keep_prob: dropout})

if step % display_step == 0:

# Calculate batch loss and accuracy

loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,

y: batch_y,

keep_prob: 1.})

print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \

"{:.6f}".format(loss) + ", Training Accuracy= " + \

"{:.5f}".format(acc))

itchat.send("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \

"{:.6f}".format(loss) + ", Training Accuracy= " + \

"{:.5f}".format(acc), wechat_name)

step += 1

with lock:

run_state = running

print("Optimization Finished!")

itchat.send("Optimization Finished!", wechat_name)

# Calculate accuracy for 256 mnist test images

print("Testing Accuracy:", \

sess.run(accuracy, feed_dict={x: mnist.test.images[:256],

y: mnist.test.labels[:256],

keep_prob: 1.}))

itchat.send("Testing Accuracy: %s" %

sess.run(accuracy, feed_dict={x: mnist.test.images[:256],

y: mnist.test.labels[:256],

keep_prob: 1.}), wechat_name)

with lock:

running = False

@itchat.msg_register([itchat.content.TEXT])

def chat_trigger(msg):

global lock, running, learning_rate, training_iters, batch_size, display_step

if msg['Text'] == u'开始':

print('Starting')

with lock:

run_state = running

if not run_state:

try:

threading.Thread(target=nn_train, args=(msg['FromUserName'], (learning_rate, training_iters, batch_size, display_step))).start()

except:

msg.reply('Running')

elif msg['Text'] == u'停止':

print('Stopping')

with lock:

running = False

elif msg['Text'] == u'参数':

itchat.send('lr=%f, ti=%d, bs=%d, ds=%d'%(learning_rate, training_iters, batch_size, display_step),msg['FromUserName'])

else:

try:

param = msg['Text'].split()

key, value = param

print(key, value)

if key == 'lr':

learning_rate = float(value)

elif key == 'ti':

training_iters = int(value)

elif key == 'bs':

batch_size = int(value)

elif key == 'ds':

display_step = int(value)

except:

pass

if __name__ == '__main__':

itchat.auto_login(hotReload=True)

itchat.run()

这段代码里面,我所做的修改主要是:

0.导入了itchat和threading

1. 把原本的脚本里网络构成和训练的部分甩到了一个函数nn_train里

def nn_train(wechat_name, param):

global lock, running

# Lock

with lock:

running = True

# mnist data reading

mnist = input_data.read_data_sets("data/", one_hot=True)

# Parameters

# learning_rate = 0.001

# training_iters = 200000

# batch_size = 128

# display_step = 10

learning_rate, training_iters, batch_size, display_step = param

# Network Parameters

n_input = 784 # MNIST data input (img shape: 28*28)

n_classes = 10 # MNIST total classes (0-9 digits)

dropout = 0.75 # Dropout, probability to keep units

# tf Graph input

x = tf.placeholder(tf.float32, [None, n_input])

y = tf.placeholder(tf.float32, [None, n_classes])

keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)

# Create some wrappers for simplicity

def conv2d(x, W, b, strides=1):

# Conv2D wrapper, with bias and relu activation

x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')

x = tf.nn.bias_add(x, b)

return tf.nn.relu(x)

def maxpool2d(x, k=2):

# MaxPool2D wrapper

return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],

padding='SAME')

# Create model

def conv_net(x, weights, biases, dropout):

# Reshape input picture

x = tf.reshape(x, shape=[-1, 28, 28, 1])

# Convolution Layer

conv1 = conv2d(x, weights['wc1'], biases['bc1'])

# Max Pooling (down-sampling)

conv1 = maxpool2d(conv1, k=2)

# Convolution Layer

conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])

# Max Pooling (down-sampling)

conv2 = maxpool2d(conv2, k=2)

# Fully connected layer

# Reshape conv2 output to fit fully connected layer input

fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])

fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])

fc1 = tf.nn.relu(fc1)

# Apply Dropout

fc1 = tf.nn.dropout(fc1, dropout)

# Output, class prediction

out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])

return out

# Store layers weight & bias

weights = {

# 5x5 conv, 1 input, 32 outputs

'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),

# 5x5 conv, 32 inputs, 64 outputs

'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),

# fully connected, 7*7*64 inputs, 1024 outputs

'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),

# 1024 inputs, 10 outputs (class prediction)

'out': tf.Variable(tf.random_normal([1024, n_classes]))

}

biases = {

'bc1': tf.Variable(tf.random_normal([32])),

'bc2': tf.Variable(tf.random_normal([64])),

'bd1': tf.Variable(tf.random_normal([1024])),

'out': tf.Variable(tf.random_normal([n_classes]))

}

# Construct model

pred = conv_net(x, weights, biases, keep_prob)

# Define loss and optimizer

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables

init = tf.global_variables_initializer()

# Launch the graph

with tf.Session() as sess:

sess.run(init)

step = 1

# Keep training until reach max iterations

print('Wait for lock')

with lock:

run_state = running

print('Start')

while step * batch_size < training_iters and run_state:

batch_x, batch_y = mnist.train.next_batch(batch_size)

# Run optimization op (backprop)

sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,

keep_prob: dropout})

if step % display_step == 0:

# Calculate batch loss and accuracy

loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,

y: batch_y,

keep_prob: 1.})

print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \

"{:.6f}".format(loss) + ", Training Accuracy= " + \

"{:.5f}".format(acc))

itchat.send("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \

"{:.6f}".format(loss) + ", Training Accuracy= " + \

"{:.5f}".format(acc), wechat_name)

step += 1

with lock:

run_state = running

print("Optimization Finished!")

itchat.send("Optimization Finished!", wechat_name)

# Calculate accuracy for 256 mnist test images

print("Testing Accuracy:", \

sess.run(accuracy, feed_dict={x: mnist.test.images[:256],

y: mnist.test.labels[:256],

keep_prob: 1.}))

itchat.send("Testing Accuracy: %s" %

sess.run(accuracy, feed_dict={x: mnist.test.images[:256],

y: mnist.test.labels[:256],

keep_prob: 1.}), wechat_name)

with lock:

running = False

这里大部分是跟原本的代码一样的,不过首先所有print的地方都加了个itchat.send来输出日志,此外加了个带锁的状态量running用来做运行开关。此外,部分参数是通过函数参数传入的。

然后呢,写了个itchat的handler

@itchat.msg_register([itchat.content.TEXT])

def chat_trigger(msg):

global lock, running, learning_rate, training_iters, batch_size, display_step

if msg['Text'] == u'开始':

print('Starting')

with lock:

run_state = running

if not run_state:

try:

threading.Thread(target=nn_train, args=(msg['FromUserName'], (learning_rate, training_iters, batch_size, display_step))).start()

except:

msg.reply('Running')

作用是,如果收到微信消息,内容为『开始』,那就跑训练的函数(当然,为了防止阻塞,放在了另一个线程里)

最后再在脚本主流程里使用itchat登录微信并且启动itchat的服务,这样就实现了基本的控制。

if __name__ == '__main__':

itchat.auto_login(hotReload=True)

itchat.run()

但是我们不满足于此,我还希望可以对流程进行一些控制,对参数进行一些修改,于是乎:

@itchat.msg_register([itchat.content.TEXT])

def chat_trigger(msg):

global lock, running, learning_rate, training_iters, batch_size, display_step

if msg['Text'] == u'开始':

print('Starting')

with lock:

run_state = running

if not run_state:

try:

threading.Thread(target=nn_train, args=(msg['FromUserName'], (learning_rate, training_iters, batch_size, display_step))).start()

except:

msg.reply('Running')

elif msg['Text'] == u'停止':

print('Stopping')

with lock:

running = False

elif msg['Text'] == u'参数':

itchat.send('lr=%f, ti=%d, bs=%d, ds=%d'%(learning_rate, training_iters, batch_size, display_step),msg['FromUserName'])

else:

try:

param = msg['Text'].split()

key, value = param

print(key, value)

if key == 'lr':

learning_rate = float(value)

elif key == 'ti':

training_iters = int(value)

elif key == 'bs':

batch_size = int(value)

elif key == 'ds':

display_step = int(value)

except:

pass

通过这个,我们可以在epoch中途停止(因为nn_train里通过检查running标志来确定是否需要停下来),也可以在训练开始前调整learning_rate等几个参数。

实在是很简单……

雷锋网版权文章,未经授权禁止转载。详情见转载须知。

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